Abstract
| Language | English |
|---|---|
| Title of host publication | 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) |
| Place of Publication | Piscataway, NJ. |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 9 Dec 2014 |
| Event | IEEE Symposium on Computational Intelligence Applications in Smart Grid - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014 |
Conference
| Conference | IEEE Symposium on Computational Intelligence Applications in Smart Grid |
|---|---|
| Abbreviated title | CIASG 2014 |
| Country | United States |
| City | Orlando |
| Period | 9/12/14 → 12/12/14 |
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Keywords
- complexity theory
- feature extraction
- hidden Markov models
- home appliances
- support vector machines
Cite this
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A low-complexity energy disaggregation method : performance and robustness. / Altrabalsi, Hana; Liao, Jing; Stankovic, Lina; Stankovic, Vladimir.
2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG). Piscataway, NJ. : IEEE, 2014. p. 1-8.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - A low-complexity energy disaggregation method
T2 - performance and robustness
AU - Altrabalsi, Hana
AU - Liao, Jing
AU - Stankovic, Lina
AU - Stankovic, Vladimir
N1 - (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).
AB - Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).
KW - complexity theory
KW - feature extraction
KW - hidden Markov models
KW - home appliances
KW - support vector machines
UR - http://www.ieee-ssci.org/CIASG.html
UR - http://ciasg.rtpis.org/
U2 - 10.1109/CIASG.2014.7011569
DO - 10.1109/CIASG.2014.7011569
M3 - Conference contribution book
SP - 1
EP - 8
BT - 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)
PB - IEEE
CY - Piscataway, NJ.
ER -